Sea Level Rise Learning Scenarios for Adaptive Decision‐Making Based on IPCC AR6

Abstract

Adaptation decision‐scientists increasingly use real‐option analysis to consider the value of learning about future climate variable development in adaptation decisions. Toward this end learning scenarios are needed, which are scenarios that provide information on future variable values seen not only from today (as static scenarios), but also seen from future moments in time. Decision‐scientists generally develop learning scenarios themselves, mostly through time‐independent (stationary) or highly simplified methods. The climate learning scenarios thus attained generally only poorly represent the uncertainties of state‐of‐the‐art climate science and thus may lead to biased decisions. This paper first motivates the need for learning scenarios by providing a simple example to illustrate characteristics and benefits of learning scenarios. Next, we analyze how well learning scenarios represent climate uncertainties in the context of sea level rise and present a novel method called direct fit to generate climate learning scenarios that outperforms existing methods. This is illustrated by quantifying the difference of the sea level rise learning scenarios created with both methods to the original underlying scenario. The direct fit method is based on pointwise probability distributions, for example, boxplots, and hence can be applied to static scenarios as well as ensemble trajectories. Furthermore, the direct fit method offers a much simpler process for generating learning scenarios from static or “ordinary” climate scenarios.

Fusion of Probabilistic Projections of Sea-Level Rise

“…The fusion could determine the timing of "cut-off probabilities", beyond which a proposed investment decision becomes undesirable (Stroombergen & Lawrence, 2022). The fusion could be transformed from a static projection to a learning scenario that can inform real options analysis (Völz & Hinkel, 2023a, 2023b. We could also produce a fusion of multi-centennial projections that extend beyond the end of the 21 st century (Turner et al, 2023).…”

Sea Level Rise in Europe: Adaptation Measures and Decision Making Principles

“…(New et. al, 2022;Marchau et al, 2019) Broadly, two categories of analytical ADM approaches exist (Völz, & Hinkel, 2023b). A first category of 655 these methods are adaptive planning methods (Walker et al, 2001), which start with a set of pre-defined adaptation options and then analyse under which future climatic developments desired objectives can be achieved.…”

“…Discussion started: 15 January 2024 c Author(s) 2024. CC BY 4.0 License.this gap have been taken byVölz and Hinkel (Völz & Hinkel., 2023b), who developed sea-level rise learning scenarios 675 based the sea-level rise scenarios of IPCC AR6.…”

Practitioner Needs to Adapt to Sea-Level Rise: Distilling Information from Global Workshops

“…According to this framework decisions can be categorized by timeframes and risk tolerances and these categories can determine the ideal information type for those decisions. For example, long-term (up to 100 years and longer) decisions with low uncertainty tolerances need upper bound information, while those long-term decisions with medium uncertainty tolerances need adaptive or learning scenariosan approach that is still being developed Volz and Hinkel, 2023. Testing this framework against stakeholder preferences could provide a useful way of furthering co-development of useful information between practitioners and SLR scientists.…”